Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines
In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the deep Q-network algorithm, to predict the face support pressure. The algorithm uses a neural network to make dec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Geosciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3263/13/3/82 |
| _version_ | 1797611530217324544 |
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| author | Enrico Soranzo Carlotta Guardiani Wei Wu |
| author_facet | Enrico Soranzo Carlotta Guardiani Wei Wu |
| author_sort | Enrico Soranzo |
| collection | DOAJ |
| description | In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the deep Q-network algorithm, to predict the face support pressure. The algorithm uses a neural network to make decisions based on the expected rewards of each action. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure whilst minimising settlement, and adapting to changes in geological and geometrical conditions. The algorithm reaches maximum performance after 400 training episodes and can be used for random geological settings without retraining. |
| first_indexed | 2024-03-11T06:29:02Z |
| format | Article |
| id | doaj.art-5eb22d17d9ec49ca892a8fa06f6313e8 |
| institution | Directory Open Access Journal |
| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-03-11T06:29:02Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-5eb22d17d9ec49ca892a8fa06f6313e82023-11-17T11:19:58ZengMDPI AGGeosciences2076-32632023-03-011338210.3390/geosciences13030082Reinforcement Learning for the Face Support Pressure of Tunnel Boring MachinesEnrico Soranzo0Carlotta Guardiani1Wei Wu2Institute of Geotechnical Engineering, University of Natural Resources and Life Sciences, 1180 Vienna, AustriaInstitute of Geotechnical Engineering, University of Natural Resources and Life Sciences, 1180 Vienna, AustriaInstitute of Geotechnical Engineering, University of Natural Resources and Life Sciences, 1180 Vienna, AustriaIn tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the deep Q-network algorithm, to predict the face support pressure. The algorithm uses a neural network to make decisions based on the expected rewards of each action. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure whilst minimising settlement, and adapting to changes in geological and geometrical conditions. The algorithm reaches maximum performance after 400 training episodes and can be used for random geological settings without retraining.https://www.mdpi.com/2076-3263/13/3/82tunnellingtunnel boring machinesupport pressureface stabilityreinforcement learningmachine learning |
| spellingShingle | Enrico Soranzo Carlotta Guardiani Wei Wu Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines Geosciences tunnelling tunnel boring machine support pressure face stability reinforcement learning machine learning |
| title | Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines |
| title_full | Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines |
| title_fullStr | Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines |
| title_full_unstemmed | Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines |
| title_short | Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines |
| title_sort | reinforcement learning for the face support pressure of tunnel boring machines |
| topic | tunnelling tunnel boring machine support pressure face stability reinforcement learning machine learning |
| url | https://www.mdpi.com/2076-3263/13/3/82 |
| work_keys_str_mv | AT enricosoranzo reinforcementlearningforthefacesupportpressureoftunnelboringmachines AT carlottaguardiani reinforcementlearningforthefacesupportpressureoftunnelboringmachines AT weiwu reinforcementlearningforthefacesupportpressureoftunnelboringmachines |